
where \(\mathcal {R}(\phi )\)  is a regularization term, and \(\lambda \)  is a hyper-parameter controlling the contribution of the regularization term.
Various penalty terms have been proposed,
however, none of them take into account of the function space of identifiability, which is at the foundation of learning (see Section REF ). Based on it, we will introduce a data-adaptive RKHS regularization method (in Section REF ). Thus, it is different from classical regularization using an ad hoc RKHS [1]}, [2]}.
